integrated molecular characterization of fumarate hydratase … · 2021. 1. 7. · 6 1. of. 429 rcc...

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Integrated Molecular Characterization of Fumarate Hydratase-deficient

Renal Cell Carcinoma

Guangxi Sun 1,#, Xingming Zhang 1,#, Jiayu Liang 1,#, Xiuyi Pan 2,#, Sha Zhu 1,#,

Zhenhua Liu 1,#, Cameron M. Armstrong 3, Jianhui Chen 4, Wei Lin 5, Banghua

Liao 1, Tianhai Lin 1, Rui Huang 6, Mengni Zhang 2, Linmao Zheng 2, Xiaoxue

Yin 2, Ling Nie 2, Pengfei Shen 1, Jinge Zhao 1, Haoran Zhang 1, Jindong Dai 1,

Yali Shen 7, Zhiping Li 7, Jiyan Liu 8, Junru Chen 1, Jiandong Liu 1,9, Zhipeng

Wang 1, Xudong Zhu 1, Yuchao Ni 1, Dan Qin 10, Ling Yang 11, Yuntian Chen 11,

Qiang Wei 1, Xiang Li 1, Qiao Zhou 2, Haojie Huang 12,＋, Jin Yao 11,

＋,*, Ni Chen

2,＋,*, Hao Zeng 1,＋,*

1. Department of Urology, Institute of Urology, West China Hospital, Sichuan

University, 610041, Chengdu, China

2. Department of Pathology, West China Hospital, Sichuan University, 610041,

Chengdu, China

3. Department of Urology and Comprehensive Cancer Center, University of

California Davis, Sacramento, 95817, CA, USA

4. Department of Urology, Fujian Medical University Union Hospital, Fuzhou,

China

5. Department of Urology, Zigong Fourth People’s Hospital, 643000, Zigong,

China

6. Department of Nuclear medicine, West China Hospital, Sichuan University,

610041, Chengdu, China

7. Department of Oncology, West China Hospital, Sichuan University, 610041,

Chengdu, China

8. Department of Biotherapy, West China Hospital, Sichuan University,

610041, Chengdu, China

Research. on July 3, 2021. © 2021 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 7, 2021; DOI: 10.1158/1078-0432.CCR-20-3788

http://clincancerres.aacrjournals.org/

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9. Department of Urology, The First Affiliated Hospital, School of Medicine,

Zhejiang University, 310009, Hangzhou, China

10. The Bioinformatics Department, Basebiotech Co., Ltd, 610000, Chengdu,

China

11. Department of Radiology, West China Hospital, Sichuan University, 610041,

Chengdu, China

12. Departments of Biochemistry and Molecular Biology and Urology, Mayo

Clinic College of Medicine and Science, Rochester, 55905, MN, USA

# These authors are co-first authors and contributed equally.

＋ These authors are co-senior authors.

* These authors are co-corresponding authors.

Running title

Genomic and epigenomic profiling of FH-deficient RCC

Corresponding Author:

Address: No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan

Province, PR China; Phone number: +86 28 85422026; Fax: +86 28

85582944.

Email address: [email protected] (Jin. Yao); [email protected] (Ni. Chen);

[email protected] (Hao. Zeng)

Conflicts of interest

The authors declare no conflicts of interest.

Words count: 3,903 words

Total number of Figures and Tables: 4 figures and 1 table in main

manuscript, 6 figures and 4 tables in supplementary appendix.



mailto:[email protected]:[email protected]:[email protected]://clincancerres.aacrjournals.org/

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Translational Relevance

Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare

but lethal malignancy, and its genetic basis is poorly understood. We

comprehensively profiled untreated primary FH-deficient RCC tissues to

characterize the molecular landscape. We identified that DNA methylation

profiles could be the biological foundation for tumor pathogenesis; hence,

compared with other RCC subtypes, methylation sequencing should be

recommended for patients with FH-deficient RCC. Moreover, FH-deficient

RCC was characterized by an immunogenic phenotype despite its low overall

mutation burden. Patients with this tumor type could benefit from immune

checkpoint blockade-based strategies. Our results provide insights into the

biology of FH-deficient RCC and pave the way for the development of novel

promising therapies.




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Abstract

Purpose: Fumarate hydratase-deficient renal cell carcinoma (FH-deficient

RCC) is a rare but lethal subtype of RCC. Little is known about the genomic

profile of FH-deficient RCC, and the therapeutic options for advanced disease

are limited. To this end, we performed a comprehensive genomics study to

characterize the genomic and epigenomic features of FH-deficient RCC.

Experimental Design: Integrated genomic, epigenomic, and molecular

analyses were performed on 25 untreated primary FH-deficient RCCs.

Complete clinicopathological and follow-up data of these patients were

recorded.

Results: We identified that FH-deficient RCC manifested low somatic mutation

burden (median 0.58 mutations per megabase), but with frequent somatic

copy number alterations. The majority of FH-deficient RCCs were

characterized by a CpG sites island methylator phenotype (CIMP), displaying

concerted hypermethylation at numerous CpG sites in genes of transcription

factors, tumor-suppressors and tumor hallmark pathways. However, a few

cases (20%) with low metastatic potential showed relatively low DNA

methylation levels, indicating the heterogeneity of methylation pattern in

FH-deficient RCC. Moreover, FH-deficient RCC is potentially highly

immunogenic, characterized by increased tumor T cell infiltration but high

expression of immune checkpoint molecules in tumors. Clinical data further

demonstrated that patients receiving immune checkpoint blockade-based

treatment achieved improved progression free-survival over those treated with

antiangiogenic monotherapy (median: 13.3 vs. 5.1 months, P = 0.03).

Conclusions: These results reveal the genomic features and provide new

insight into potential therapeutic strategies for FH-deficient RCC.




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Introduction 1

Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is 2

a rare subtype of kidney cancer characterized by pathologic germline/somatic 3

mutation of the fumarate hydratase (FH) gene, with a lack of FH staining on 4

immunohistochemistry (IHC). FH-deficient RCC is difficult to diagnose, since it 5

frequently shows mixed pathologically architectural patterns; therefore, it is 6

highly susceptible to be misdiagnosed as other RCC subtypes (1,2). Clinically, 7

most cases of FH-deficient RCC are very aggressive and frequently present 8

with locally advanced or metastatic disease at initial diagnosis (3). 9

Unfortunately, owing to its rarity, little is known about its genetic basis, and 10

there are currently no standard treatment strategies for patients with advanced 11

disease. 12

To date, our knowledge of the genetic basis of FH-deficient RCC mainly 13

comes from the reports of the Cancer Genome Atlas (TCGA) database. Using 14

comprehensive genomic analyses, TCGA identified a distinct group of papillary 15

RCC characterized by a CpG island methylator phenotype (CIMP), of which 16

five cases were found to harbor germline or somatic FH mutation (4). However, 17

due to the limited cases in the TCGA cohort, specific research aiming to 18

explore the genomic landscape of FH-deficient RCC is still lacking. In this 19

study, we performed a high-throughput genomic and epigenomic analysis of 20

untreated primary FH-deficient RCC tumors from multi-institutional patient 21

cohorts in order to provide molecular insights into FH-deficient RCC and 22

suggest paths for the development of biology-driven precision medicine. 23

24

Materials and Methods 25

Patient Identification 26

We cooperated with nine medical centers to collect FH-deficient RCC 27

samples. All suspicious RCC tumors were referred for pathologic consultation 28

at our medical center (West China Hospital). Between 2014 and 2019, a total 29




6

of 429 RCC samples were retrospectively analyzed (Figure S1). All candidate 1

cases were reviewed by two experienced uropathologists (Ni Chen and 2

Mengni Zhang) and subsequently subjected to immunohistochemical staining 3

(IHC) of FH protein. Diagnosis of FH-deficient RCC was confirmed by Sanger 4

sequencing for FH mutation (Figure S2). Clinicopathological data were 5

retrospectively collected. A total of 25 cases with FH-deficient RCC were 6

identified. Untreated primary tumor tissues and matched germline samples 7

were collected. DNA and methylation sequencing of formalin-fixed 8

paraffin-embedded (FFPE) tissues were subsequently performed. Twenty-two 9

FFPE tumor tissues and matched adjacent normal tissues (n = 12) or blood 10

samples (n = 10) were collected for whole-exome sequencing (WES) to 11

assess the genomic mutation characteristics. Targeted sequencing was 12

performed on two biopsy tumor tissues (FHRCC18 and FHRCC24) and 13

matched blood samples due to insufficient tumor DNA for WES. One patient 14

(FHRCC14) had only a blood sample for WES due to insufficient tumor tissue. 15

EPIC array was performed on 20 tumors and 12 adjacent normal tissues. The 16

study was conducted in accordance with the Declaration of Helsinki and all 17

clinical samples were acquired with written informed consents under 18

permission from the Ethics Committee of West China Hospital of Sichuan 19

University. All patients or family members provided written consent for genetic 20

analysis. 21

22

Clinicopathological characteristics 23

Clinicopathological data were retrospectively collected, including age, 24

gender, family history, metastatic sites, TNM stage, histological type, ISUP 25

grade, surgery type, and systemic treatment type. Synchronous metastasis 26

was defined as the diagnosis of a distant metastasis at the time of the primary 27

renal cell carcinoma diagnosis. Metachronous metastasis was defined as the 28

metastasis occurred after three months postoperatively. 29




7

1

Outcomes 2

Patients visited the urologists for regular evaluations (physical 3

examination, radiologic assessment and laboratory tests) every 4-6 weeks. 4

Response was defined by Response Evaluation Criteria in Solid Tumors 5

(RECIST) version 1.1 (5). The end-points were objective response rate (ORR), 6

disease control rate (DCR), disease-free survival (DFS), progression-free 7

survival (PFS), and overall survival (OS). ORR and DCR were assessed 8

according to RECIST criteria 1.1. ORR was defined as the proportion of 9

patients with complete or partial responses as the best radiological response 10

to therapy. DCR encompassed the proportion of patients who achieved an 11

objective response plus stable disease. DFS was defined as the time from 12

diagnosis to local or regional recurrence or distance metastasis or death from 13

any cause. PFS was defined as the time from initial diagnosis to disease 14

progression or death. First-line PFS was defined as the time from the start of 15

first-line systemic treatment to disease progression or death. OS was defined 16

as the time from initial diagnosis to all-cause death. Systemic treatment OS 17

was defined as the time from the start of systemic treatment to all-cause death. 18

19

WES and methylation sequencing 20

For samples performing WES, high-quality genomic DNA was extracted 21

using the GeneRead DNA FFPE Kit (180134, QIAGEN, Hilden, GER) 22

according to the manufacturer’s specifications. Exome capture was performed 23

using Agilent SureSelect Human All ExonV6 kit (Technologies, CA, USA) 24

followed by paired-end sequencing using the Illumina Hiseq Xten platform 25

(Illumine Inc, CA, USA). For panel sequencing, DNA was extracted and 26

estimated by a targeted sequencing strategy capturing all exons of 642 27

tumor-related genes (Table S1). Human Infinium MethylationEPIC BeadChip 28

(Illumina) was used to analyze DNA methylation. Extracted DNA was treated 29




8

with RNase A/T1 Mix (Thermo Scientific, Oberhausen, Germany) and 1

subsequently purified using the Genomic DNA Clean & Concentrator™−10 Kit 2

(Zymo Research, CA, USA). Bisulfite pyrosequencing was used to validate the 3

results of the microarray analysis. The WES data generated in this paper have 4

been deposited at the NCBI Sequence Read Archive (SRA) hosted by the NIH 5

(SRA accession: PRJNA533711). The methylation data reported in this paper 6

were deposited in the Gene Expression Omnibus (GEO) database (accession 7

numbers GSE155207). More detailed materials and methods can be found in 8

the supplementary methods. 9

10

Immunohistochemistry (IHC) 11

IHC for FH, CD8, CD4 and PD-L1 were performed using an automatic 12

staining platform, Ventana NexES (Roche, Basel, CH). Commercially available 13

primary anti-FH (1: 800, sc-100743, Santa Cruz, TX, USA), CD8 (1:100, clone 14

C8/144B, Dako, Copenhagen, DEN), CD4 (ZM-0418, 1:100, Zhongshan, 15

Beijing, CHN) and PD-L1 (740-4859, 1:40, Roche) were used in this study. 16

PD-L1 was determined by quantification of the density of positive cells (defined 17

as the number of positive tumor cells per mm2) and the percentage of tumor 18

cells. PD-L1 positivity was defined as PD-L1 expression ≥ 1%. CD4 and CD8 19

were determined by quantification of the density of positive cells defined as the 20

number of positive tumor infiltrating lymphocytes (TIL) per mm2 and the 21

percentage of TILs. 22

23

Multiple immunofluorescence 24

Multiplex staining and multispectral imaging were performed to identify the 25

different types of immune cells. Multiplex immunofluorescence staining was 26

obtained using PANO 7-plex kit (0004100100, Panovue, Beijing, CHN). 27

Different primary antibodies, including CD8 (clone C8/144B, Panovue), CD56 28

(clone 123C3, Panovue), HLA-DR (clone EPR3692, Panovue,), CD68 29




9

(Panovue, clone BP6036) and PanCK (0057000050, Panovue) were 1

sequentially applied, followed by horseradish peroxidase-conjugated 2

secondary antibody incubation and tyramide signal amplification. The slides 3

were microwave heat-treated after each Trichostatin A (TSA) operation. Nuclei 4

were stained with 4′-6′-diamidino-2-phenylindole (D9542, DAPI, Sigma, 5

Darmstadt, GER) after all the human antigens had been labeled. The stained 6

slides were scanned via the Mantra System to obtain multispectral images 7

(PerkinElmer, Massachusetts, USA). The scans were combined to build a 8

single stack image. All slides were scanned at an absolute magnification of × 9

200 and × 100. Images of unstained and single-stained sections were used to 10

extract the spectrum of autofluorescence of tissues and each fluorescein, 11

respectively. The extracted images were further used to establish a spectral 12

library required for multispectral unmixing by Inform image analysis software 13

(PerkinElmer). The CD8, macrophage and NK cells expression were quantified 14

by the number of positive cells per mm2 and by the number of positive cells per 15

number of nucleated cells. 16

17

Sanger sequencing 18

The presence of somatic mutations of FH was validated by Sanger 19

sequencing. For this analysis, primers to amplify a 220-bp fragment covering 20

each exon (1-10) of FH gene were designed (Table S2). PCR was performed 21

with the following thermal cycling conditions: a 95°C for 5 min; 35 cycles of 22

95°C for 20 s; 60°C for 30 s and 72°C for 30 s; and a 72°C for 7 min. PCR 23

amplification of 10ng of genomic DNA was performed using the Premix Taq™ 24

(RR901A, Takara, Shiga, JPN) on a Veriti96 PCR Cycler (Life Technologies, 25

Massachusetts, USA). PCR fragments were purified with ChargeSwitch™ 26

PCR Clean-Up Kit (CS12000, Invitrogen, Oberhausen, GER), and the 27

sequencing reactions were performed on an ABI 3730XL automatic sequencer 28

(Life Technologies) according to the manufacturer’s instructions. All analyses 29




10

were performed in duplicate. Sequences of the forward and reverse strands 1

were analyzed using Chromas (Technelysium, AUS). 2

3

Bisulfite pyrosequencing 4

Bisulfite modification of genomic DNA isolated from 30 samples was 5

performed by standard methods. PCR primers were designed with the 6

PyroMark Assay Design V.2.0 software (QIAGEN) (Table S3). PCR products 7

were pyrosequenced with the PyromarkTM Q24 system (QIAGEN), according 8

to the manufacturer’s protocol. 9

10

Statistics 11

Fisher’s exact test was used to compare the difference between 12

categorical variables. Median survival was estimated using the Kaplan–Meier 13

method and the difference was tested using the log-rank test. All tests were 14

two-sided and performed using R software v.3.2.3, SPSS v.16.0 and 15

GraphPad V.6.0. A P-value < 0.05 was considered statistically significant. 16

17

Results 18

Patient characteristics 19

From 2014 to 2019, a total of 25 patients with FH-deficient RCC were 20

identified from 429 suspicious cases (Figure S1A). Among them, 16 (64%) 21

patients had germline FH mutations, eight (32%) patients had somatic FH 22

mutations, and one patient (FHRCC20) had a germline mutation (c.1256C>T) 23

and a second hit of an additional frameshift mutation (c.642delA) in the tumor. 24

Baseline characteristics of all patients are summarized in Table 1 and S4. The 25

median age at initial diagnosis was 38 years (range: 13-66 years) and the male: 26

female ratio was 1.8:1. Male patients tended to be older at onset of primary 27

RCC (P = 0.026, Figure S1B). At the end of follow-up (median: 14.3 months, 28

range 3.8-137.1 months), 21 patients (84%, 21/25) presented with metastases, 29




11

including 14 synchronous metastases and 7 metachronous metastases. The 1

most common metastases were lymph nodes (76%, 16/21) and bones (48%, 2

10/21). Five patients died by the end of follow-up. We found that those 3

diagnosed over the age of 40 years old had a higher incidence of metastasis 4

(P = 0.032, Figure S1C) and worse prognosis compared with the younger 5

patients (Figure S1D and E). 6

7

The somatic mutational landscape of FH-deficient RCC 8

WES was performed on 22 untreated primary tumors and matched 9

germline samples. A median of 31 somatic mutations per tumor (range, 1-156) 10

was identified, corresponding to an average of 0.58 mutations per megabase 11

(range, 0.02–2.60) (Figure 1A). A total of 27 FH mutations were identified, 12

including 15 missense mutations, 5 frame-shift indels, 5 nonsense mutations, 13

1 splice site change, and 1 large deletion (Figure 1B). Apart from FH, the most 14

frequently mutated genes in FH-deficient RCC were TTN (17%, 4/24) and NF2 15

(13%, 3/24). Assessment of mutations within pathways demonstrated a high 16

number of alterations involving chromatin modification (33%, 8/24), 17

RTK-RAS-PI3K (33%, 8/24) and DNA damage repair pathway (29%, 7/24). 18

Signature analysis identified that signature 6 contributed the most to these 19

base substitutions, followed by signature 22 and signature 7, indicating that the 20

defective DNA mismatch repair (signature 6), ultraviolet light exposure 21

(signature 7) and aristolochic acid exposure (signature 22) might contribute to 22

in the development of FH-deficient RCC (Figure S3A and B). 23

Somatic copy number alterations (SCNAs) analysis revealed that 24

FH-deficient RCC had frequent copy number loss of 1p, 8q, 10p, 15q and gain 25

of 4p, 7q, 11q, and 19p (Figure 1C). Of note, frequent deletion of genes 26

involving chromatin modification (KDM4B and KDM5A), SWI/SNF complex 27

(SMARCA4 and ARID1A), DNA demethylases (TET1) and DNA damage repair 28

pathway (ERCC1, ERCC2, XRCC1 and ATM) and cell cycle regulation 29




12

(CDKN2C and CDKN2D) were identified in this cohort (Figure 1C). 1

We further investigated the relationship between somatic mutations, 2

SCNAs, and clinical features. Compared with young patients (< 40 years old), 3

tumors of older patients (≥ 40 years old) tended to have a higher somatic 4

mutation burden (median mutations per megabase: 0.75 vs. 0.27, Figure 1A); 5

however, the difference did not reach a statistical significance (P = 0.051). 6

Patients older than 40 years old also had increased chromatin instability than 7

the younger patients (P = 0.047, Figure S3C and D). These results may reveal 8

why older patients have poorer outcomes. 9

10

DNA methylation alterations associated with the pathogenesis of 11

FH-deficient RCC 12

EPIC array was performed on 20 FH-deficient RCC tumors with adjacent 13

normal tissues. The study of the methylation profiles revealed that FH-deficient 14

RCCs had a sharp global hypermethylation compared to adjacent normal 15

tissues in all epigenomic structures, except for open sea (Figure S4A). In 16

contrast to control samples, we identified a total of 199,372 differentially 17

methylated CpG sites (CpGs) in FH-deficient RCCs, of which 66% (132,220) 18

were hypermethylated CpG sites, whereas only 34% (67,152) were 19

hypomethylated CpGs (Figure 2A, Figure S4B and C). 20

Gene ontology enrichment analysis revealed that hypermethylated CpGs 21

were significantly enriched in genes related to DNA-binding transcription 22

activity and sequence-specific DNA binding (Figure 2B), indicating the 23

transcriptional regulators might participate in the pathogenesis of FH-deficient 24

RCC. Moreover, we found part of hypermethylated CpGs located in multiple 25

well-established tumor suppressor genes (CDKN2A, APC, MGMT and TP53) 26

and small RNAs (MIR200a, MIR200b, MIR200c and MIR429) (Figure S4D 27

and E). Bisulfite pyrosequencing was used to validate DNA methylation levels 28

in four gene regions in three hypermethylated loci (CDKN2A, APC and MGMT) 29




13

and one hypomethylated locus (IL4R). A high correlation was observed 1

between values obtained from microarray and pyrosequencing (Figure S4F). 2

Furthermore, functional enrichment analysis identified an enrichment of genes 3

of hallmark pathways in cancer for aberrant methylation, including WNT, 4

mTOR, TGF-β, NOTCH and EMT pathways (Figure 2B and Figure S4G). 5

Hypermethylated CpGs also mapped to genes involving the DNA-damage 6

repair pathways (Figure S4H). Gene set enrichment analysis (GSEA) using 7

genome array data further revealed that genes linked to EMT, mTORC1, 8

DNA-damage repair and glycolysis pathways were upregulated in FH-mutated 9

RCCs versus normal tissues (Figure 2C). 10

11

Identification of the methylation patterns of FH-deficient RCC 12

To illuminate the DNA methylation pattern of FH-deficient RCC, we 13

merged data from our FH-deficient RCC and TCGA-KIRP cohorts. Using 14

unsupervised clustering analysis, we identified three DNA methylation clusters 15

(Figure 2D). The majority of our FH-deficient RCC cases (80%,16/20) and all 16

of the CIMP-RCC cases in KIRP cohort were categorized into the CIMP cluster, 17

exhibiting a global DNA hypermethylation phenotype. This finding supports 18

that most FH-deficient RCCs are largely characterized by a CIMP. However, a 19

small proportion of our FH-deficient RCC cases (20%,4/20) were classified into 20

cluster 1, manifested a relatively low genome-wide DNA methylation, indicating 21

that not all FH-deficient RCCs exhibited CIMP. To further validated these 22

findings, a T-distributed stochastic neighbor embedding analysis was 23

performed to integrated ours and all TCGA RCC subtypes data. The results 24

confirmed that the majority of FH-deficient RCCs were distributed in the 25

CIMP-RCC cluster, while the presence of two different DNA methylation 26

profiles existed in our FH-deficient RCCs (Figure 2E). Notably, we found 27

patients in the non-CIMP group had a lower incidence of metastasis compared 28

to the CIMP group (25% vs. 94%, P = 0.013). Three patients in the non-CIMP 29




14

group did not develop recurrences or metastases up to the end of follow-up 1

(median: 19.7 months), with one patient (FHRCC03) followed for over six 2

years after nephrectomy. Morphologically, the majority of CIMP tumors 3

(69%,11/16) showed a predominated papillary pattern, but only one non-CIMP 4

tumor (25%, 1/4) showed a dominated papillary pattern. Two non-CIMP tumors 5

(FHRCC03 and FHRCC12) were presented with a dominated low-grade 6

oncocytic pattern, which has been reported with a favorable outcome (2,6,7). 7

Methylation profiling analysis indicated that CIMP tumors had sharp global 8

hypermethylation compared to non-CIMP tumors in all epigenomic structures, 9

except for open seas (Figure S5A and B). Signature analysis further identified 10

a total of 234,563 different signatures between these two methylation groups 11

(Figure S5C). 12

13

FH-deficient RCC manifests high immune infiltration and imprints of 14

immune evasion 15

As mentioned above, a fraction of CpGs were hypomethylated in 16

FH-deficient RCC. Strikingly, these hypomethylated CpGs were enriched in 17

genes involving the inflammation pathway and cytokine activity (Figure 3A). 18

GSEA analysis of the genome array further validated the increased expression 19

of TNF-α-NF-κB and inflammatory response signaling in FH-mutated RCC, 20

indicating the enhanced anti-tumor inflammatory signature in FH-deficient 21

RCC (Figure 3A). Using immunohistochemistry (IHC) staining, we identified 22

that the majority of tumors (96%, 24/25) showed enriched CD8+ T and CD4+ T 23

cell infiltration. Sixty-four percent (16/25) of tumors had extensive CD8+ T cell 24

infiltration (CD8+ T cells ≥ 100 mm2) (Table S4 and Figure 3B). In situ multiple 25

immunofluorescence staining further revealed the increased immune-infiltrated 26

in FH-deficient RCC, with marked activated NK (CD56+) cells and 27

macrophages (CD68+/HLA-DR+) infiltrations in tumor stroma (Figure 3C). 28

Importantly, GSEA analysis confirmed that IFN-γ response and IFN-γ 29




15

dependent cancer immunostimulation pathways (IL-6–JAK-STAT3) were 1

significantly enriched in FH-mutated RCC, indicating the presence of activated 2

T cell signature. (Figure S6A). 3

Considering that the production of IFN-γ can induce PD-L1 expression 4

and subsequently leads to immune evasion (8), we next checked the 5

expression of PD-L1 in FH-deficient RCC. Intriguingly, we found that PD-L1 6

associated CpGs were hypomethylated modified in tumors (Figure S6B). In 7

addition, hypomethylated CpGs also mapped to other immune inhibitory 8

checkpoint genes (including PDCD1L2 and CTLA4, Figure 3D and Figure 9

S6B). IHC experiments further demonstrated a high PD-L1 positive rate (80%, 10

20/25) in tumors (Table S4). Of note, nearly half of tumors (48%,12/25) 11

exhibited type II tumor immune microenvironment (TIME) (9), characterized by 12

massive CD8+ T cell infiltration but positive PD-L1 expression in tumors 13

(Figure 3D). Collectively, our data demonstrated that the anti-tumor immunity 14

in FH-deficient RCC might be largely blocked by PD-1/PD-L1 pathway 15

mediated suppression mechanism, suggesting the potential of PD-1/PD-L1 16

inhibitors. 17

18

Patients with metastatic FH-deficient RCC benefit from immune 19

checkpoint blockade-based immunotherapy 20

At the end of follow-up, 18 patients with metastatic diseases received 21

systemic therapy. First-line therapy was tyrosine kinase inhibitor (TKI) 22

monotherapy (67%, 12/18), anti-PD-1 inhibitor plus TKI (27%, 5/18) and 23

anti-PD-1 inhibitor monotherapy (6%, 1/18). For those receiving first-line 24

therapy, the DCR was higher in patients receiving immune checkpoint 25

blockade (ICB)-based treatment (anti-PD-1 inhibitor + TKIs or anti-PD-1 26

inhibitor monotherapy) versus those on TKIs monotherapy (DCR: 100% vs. 27

58.3%, Figure 4A). Moreover, median first-line PFS was superior in the 28

ICB-based treatment group compared with that in TKIs group (median: 13.3 vs. 29




16

5.1 months, P = 0.024, Figure 4B). All patients (6/6, 100%) in the ICB-based 1

first-line treatment group achieved a PFS ≥ 6 months, higher than those (5/12, 2

42%) in the TKIs group (Figure 4C and a typical case was illustrated in Figure 3

4D). The median OS of systematic treatment was also longer in the ICB-based 4

group compared to the TKIs group (median: not reached vs. 21.4 months); 5

however, the differences did not reach a statistical significance (P = 0.209, 6

Figure 4B). Two patients received additional anti-PD-1 therapy plus TKIs after 7

disease progression during first-line TKIs monotherapy; remarkably, both of 8

them achieved favorable disease control (PFS: 13.5 and 10.3 months, 9

respectively). 10

11

Discussion 12

FH-deficient RCC is a rare but lethal malignancy, and its genetic basis is 13

poorly understood. In the present study, we performed comprehensive 14

molecular profiling to characterize the genomic foundation of FH-deficient RCC. 15

We determined that FH-deficient RCC was a genetically simple tumor with a 16

relatively low mutation burden, but its distinct epigenetic features could be the 17

biological foundation for tumor pathogenesis. Moreover, FH-deficient RCC was 18

an intrinsically immunogenic tumor that is likely to benefit from ICB-based 19

treatment strategies. Our study provides novel insights into the molecular 20

landscape of FH-deficient RCC and identifies promising therapeutic targets for 21

potential exploitation. 22

Although with low somatic mutation burden, we identified increased 23

SCNAs in FH-deficient RCC, with significant loss of 1p, 8q, 10p, 15q and gain 24

of 4p, 7q, 11q, and 19p. Previous studies identified that a subgroup of papillary 25

type 2 RCCs were characterized by a high degree of aneuploidy and frequent 26

loss of 9p (4), which was not significantly altered in our FH-deficient RCCs. 27

The different SCNAs patterns between FH-deficient RCC and papillary type 2 28

RCC indicates the distinct genomic alterations during tumorigenesis, although 29




17

the majority of FH-deficient RCC was predominantly of type 2 histology. 1

Recent studies also showed an increased chromosome 22 loss in CIMP-RCC 2

(10), however, it was not a significant event in our cohort. This discrepancy 3

may reflect the heterogeneity of CIMP-RCC and warrant further investigation. 4

Loss-of-function mutations in FH resulting in the accumulation of fumarate 5

impairs the function of α-KG-dependent dioxygenases, such as histone and 6

DNA demethylases, which disrupts the normal epigenetic genes regulation 7

(11). Consistent with this, we identified FH-deficient RCC-specific DNA 8

methylation changes within a broad panel of transcription factors, as well as 9

genes significantly associated with tumor progression and patient outcomes, 10

including CDKN2A, MGMT, APC and TP53. The loss of CDKN2A by either 11

mutation, deletion or promoter hypermethylation occurred in 15.8% RCCs and 12

correlated with decreased survival in all RCC histological subtypes (10). Of 13

note, we observed that CDKN2A promoter hypermethylation is a common 14

event in FH-deficient RCCs. Loss of CDKN2A can be targeted by inhibitors of 15

cyclin-dependent kinase 4/6 (CDK4/6) (12), suggesting the potential of 16

CDK4/6 inhibitors for FH-deficient RCCs. Also, it should be pointed out that it 17

remains unclear if the loss of CDKN2A could be a predictor for response to 18

CDK4/6 inhibitors, and still warrant further investigation(13). Currently, 19

epigenetic agents have been approved for the treatment of hematological 20

malignancies (14) and are now undergoing testing in HLRCC 21

(ClinicalTrials.gov identifier NCT03165721). Our data imply that these aberrant 22

DNA methylations could be the biological foundation of FH-deficient RCC, 23

highlighting the potential of demethylation agents for treating this disease. 24

Another important finding of our study is that FH-deficient RCC exhibits a 25

distinct immunophenotype. Albeit with low mutation burden, most FH-deficient 26

RCCs exhibited typical types II TIME, characterized by increased levels of 27

tumor infiltrating lymphocytes and activated chemotaxis signaling, but with 28

high expression level of PD-L1. Therefore, it is reasonable to assume that 29




18

anti-PD-1/PD-L1 therapy can be a potential treatment option for patients with 1

FH-deficient RCC (9,15). Indeed, we observed a favorable PFS for patients 2

receiving ICB-based treatment compared to those receiving antiangiogenic 3

monotherapy. Mechanistically, this immunogenicity may be mediated by 4

epigenomic and metabolic alterations. Using the TCGA data, Singh et al. found 5

that hypomethylation of genes was associated with immune function in 6

papillary RCC (16). Recent studies also show that tricarboxylic acid cycle 7

metabolites, including fumarate, can regulate methylation status in immune 8

cells and facilitate transcription and expression of proinflammatory factors 9

(17-20). Similarly, we observed epigenetically enhanced genes involved in 10

pro-inflammatory and immune suppression pathways in FH-deficient RCC. 11

Taken together, our data suggest that FH-deficient RCC is intrinsically 12

immunogenic and could be targeted by ICB-based immunotherapy. Given the 13

global hypermethylation phenotype, co-administration of epigenomic agents 14

could be a promising strategy to improve the efficacy of PD-1/PD-L1 inhibitors. 15

In addition to the treatments mentioned above, there are other potential 16

therapeutic targets according to our findings. Mutation of the Hippo pathway 17

tumor suppressor, NF2, was detected in several tumors. Thus, targeting NF2 18

(e.g. Dasatinib) (21) may be effective in a subset of patients. Moreover, 19

preclinical studies suggested that elevated fumarate could suppress the 20

homologous recombination DNA repair pathway through inhibition of 21

demethylases (22,23). We observed mutation and frequently aberrant 22

methylation of genes involved in DNA damage repair pathways in FH-deficient 23

RCC, which supports the poly (ADP)-ribose polymerase inhibitors for targeted 24

therapy. Additionally, both ours and previous studies showed the activation of 25

glycolysis (4,24,25) and mTOR signaling in FH-deficient RCCs. Results from a 26

single-center phase II trial (ClinicalTrials.gov Identifier: NCT01130519) and 27

retrospective studies (26,27) reported the combination of bevacizumab plus 28

erlotinib could achieve fair objective response for patients with FH-deficient 29




19

RCC, indicating the potential therapeutic value of this combination therapy. 1

In summary, the current study expands our knowledge of the FH-deficient 2

RCC somatic alteration landscape, emphasizes the importance of DNA 3

methylation profiles for tumor pathogenesis and molecular classification, and 4

reveals its immunogenic phenotype. Our study represents a step forward in 5

understanding the biology of FH-deficient RCC and provides a genetic basis 6

for biomarker classification and potential therapeutic strategies. 7

8

Acknowledgements 9

This work was supported by the Natural Science Foundation of China (NSFC 10

81672547, 81872107, 81872108, 81974398, 81972502 and 81902577), China 11

Postdoctoral Science Foundation (2020M673239) and 1.3.5 project for 12

disciplines of excellence, West China Hospital, Sichuan University 13

(No.0040205301E21). The authors thank all patients involved in this study, as 14

well as their families/caregivers. We offer sincere gratitude to Prof. Allen C. 15

Gao from UC Davis Medical Center for the constructive suggestions and 16

careful revision for this manuscript. 17

18

Author contributions 19

Study concept and design: S.G.X., X.M.Z., J.Y.L., X.Y.P., S.Z., Z.H.L., H.J.H, 20

J.Y., N.C., H.Z.; 21

Methodology, acquisition, analysis, or interpretation of the data: S.G.X., X.M.Z., 22

J.Y.L., X.Y.P., S.Z., Z.H.L., J.H.C., W.L., B.H.L., T.H.L., R.H., X.X.Y., M.N.Z., 23

L.N., P.F.S., J.G.Z., H.R.Z., J.D.D., Y.L.S., Z.P.L., J.Y.L., J.R.C., J.D.L., Z.P.W., 24

X.D.Z., Y.C.N., D.Q., L.Y., Y.T.C., H.J.H, J.Y., N.C., H.Z.; 25

Financial support: S.G.X., R.H., Q.Z., N.C., H.Z.; 26

Drafting of the manuscript: S.G.X., X.M.Z., J.Y.L., X.Y.P., S.Z., Z.H.L.; 27

Critical revision of the manuscript for important intellectual content: S.G.X., 28

X.M.Z., J.Y.L., X.Y.P., S.Z., Z.H.L., H.J.H, C.M. A., Z.H.L., J.Y., N.C., H.Z.; 29




20

Statistical analysis: S.G.X., X.M.Z., J.Y.L., X.Y.P., S.Z., Z.H.L., J.G.Z. 1

Study supervision: Q.W, X.L., Q.Z., H.J.H, J.Y., N.C., H.Z. 2

3




21

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25

Figure legends

Figure 1. Somatic alterations in FH-deficient RCC.

(A) Oncoplot describes somatic genomic alterations of the FH-deficient RCC

(n = 24). Recurrently mutated genes, which are grouped by molecular

pathways, are shown on the left. Their mutation frequencies in patients with

onset of RCC < 40 years and ≥ 40 years old are shown on the right. The top

histogram shows the number of somatic mutations in each individual sample.

The middle panel shows the clinicopathological features. The bottom

histogram shows the mutation spectrum. Red arrow indicates two samples

(FHRCC18 and FHRCC24) performing panel test. See also Table S1 and S4.

(B) The types and relative positions of somatic mutations in the predicted FH

protein.

(C) Copy-number profile shows amplifications (red) and deletions (blue) in all

chromosomes for FH-deficient RCC. Cancer-related genes located within

corresponding genomic regions are indicated. For genes located in 19

chromosome, the detailed locations are: AKT2 (19q13.2), ARID3A (19p13.3),

CDKN2D (19p13.2), ERCC1 (19q13.32), ERCC2 (19q13.32), KDM4B

(19p13.3), SMARCA4 (19p13.2), XRCC1 (19q13.31).

FH = fumarate hydratase, RCC = renal cell carcinoma, IHC =

immunohistochemistry.

Figure 2. FH-deficient RCCs manifest a distinct methylation phenotype.

(A) A volcano plot comparing DNA methylation in tumors versus paired

adjacent normal tissues. Significantly hypermethylated CpG sites are colored

in red, whereas significantly hypomethylated CpG sites are colored in blue. q

values (-log10 p) were calculated using paired two-sided moderated Student’s

t-test.

(B) Gene ontology enrichment analysis of differentially methylated CpG sites.

Enrichment q values (-log10 p) are calculated by hypergeometric test. See also




26

Figure S4.

(C) GSEA enrichment scores of glycolysis, EMT, DNA-damage repair and

mTORC1 pathways in HLRCC versus normal tissues.

(D) One-dimensional hierarchical clustering of the 2,000 most variant DNA

methylation probes reveal three clusters of tumors from TCGA KIRP cohort (n

= 275) and our FH-deficient RCC cohort (n = 20). In contrast to cluster 1 and 2,

cluster 3 shows widespread DNA hypermethylation patterns characteristic of

CIMP-associated tumors. Adjacent normal tissues (n = 12) are included in the

heatmap but did not contribute to the unsupervised clustering. Each row

represents a probe; each column represents a sample.

(E) T-distributed stochastic neighbor embedding analysis of 842 TCGA RCC

samples and 20 FH-deficient RCC samples performed on 2,000 reference

probes illustrates three distinct clusters.

FH = fumarate hydratase, RCC = renal cell carcinoma, GSEA = gene set

enrichment analyses, EMT = epithelial-mesenchymal transition, TCGA = The

Cancer Genome Atlas, HLRCC = hereditary leiomyomatosis and renal cell

carcinoma, KICH = chromophobe renal cell carcinoma, KIRC = renal clear cell

carcinoma, KIRP = renal papillary cell carcinoma, CIMP = CpG island

methylator phenotype.

Figure 3. FH-deficient RCCs exhibit intrinsic immunogenic phenotype

(A) Bubble chart (upper) shows the significantly hypomethylated CpGs in

FH-deficient RCCs. Enrichment q values (-log10 p) are calculated by

hypergeometric test. GSEA enrichment scores (lower) of TNF-α-NF-κB and

inflammation response signaling in HLRCC versus normal tissues. See also

Figure S6.

(B) Representative IHC demonstrating the typical TIME type II in CIMP

(FHRCC13) and non-CIMP tumors (FHRCC12) in our cohort, is characterized

by PD-L1 positive expression and massive CD8+ and CD4+T cell infiltrations.




27

TIME type II is defined as PD-L1 positive in tumors and massive CD8+ T cell

infiltration (>100 cells/mm2). Magnification X200. Scale bar = 100μm.

(C) Representative immunofluorescence demonstrating the presence of

overall CD8+ T cell, tumor-associated macrophage (CD68 and HLA-DR) and

NK cell (CD56) infiltration in two selected samples (FHRCC16 and FHRCC19)

in our cohort. Magnification X400. Scale bar = 100μm.

(D) Heatmap shows the immune features among FH-deficient RCCs (n = 25).

The top panel shows the number of CD8+ and CD4+ T cell infiltrations, PD-L1

expression and TIME type. The middle panel shows the methylation status of a

CpG site in the promoter region of immune modulation associated genes. The

bottom histogram shows the FH mutation type, FH protein expression and

CIMP status.

FH = fumarate hydratase, RCC = renal cell carcinoma, HLRCC = hereditary

leiomyomatosis and renal cell carcinoma, GSEA = gene set enrichment

analyses, TIME = tumor immune microenvironment, CIMP = CpG island

methylator phenotype.

Figure 4. Responses to systemic treatment in patients with metastatic

FH-deficient RCC.

(A) Histogram shows the best radiographic response in patients receiving

first-line TKIs and ICB-based treatment groups. The best response is

assessed via Response Evaluation Criteria in Solid Tumors 1.1. ICB-based

treatments include patients receiving anti-PD-1 inhibitor plus TKIs or anti-PD-1

inhibitor monotherapy.

(B) Kaplan-Meier curves show the first-line PFS (left) and OS (right) between

patients receiving first-line TKIs (red) and ICB-based (green) treatments.

(C) Swimmer plot depicts the PFS of individual patients receiving first-line

systematic treatments. Each patient’s tumor response at the end of follow-up

(PR, SD or PD) is noted. Vertical line indicates PFS at 6 months.




28

(D) Baseline imaging in a patient (FHRCC16) before initiation of systematic

treatment, with multiple para-aorta lymphatic metastases. The patient

subsequently received the combination of Sintilimab plus Axitinib treatment.

Imaging performed at 3 months after the combination therapy demonstrates

continued objective response (33.9% by RECIST v1.1).

FH = fumarate hydratase, RCC = renal cell carcinoma, PR = partial response,

SD = stable disease, PD = progression disease, ORR = objective response

rate, PFS = progression-free survival, OS = overall survival, TKIs = tyrosine

kinase inhibitors, ICB = immune checkpoint blockade.




Table 1. Baseline characteristics of patients with FH-deficient RCC

Patient Characteristics N(%)

Total number of patients 25(100)

Age at diagnosis, years (Median, range) 38(13-66)

Male 16(64)

Female 9(36)

Patients with skin leiomyoma 2(8)

Female patients with uterine leiomyoma 5(55.6)

Primary tumor size, cm (Median, range) 8.8(3-16.9)

T stage

Published OnlineFirst January 7, 2021.Clin Cancer Res Guangxi Sun, Xingming Zhang, Jiayu Liang, et al. Hydratase-deficient Renal Cell CarcinomaIntegrated Molecular Characterization of Fumarate

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